The Machine Learning and Optimization Group of Microsoft Research pushes the state of the art in machine learning and optimization. Our work spans the space from theoretical foundations of machine learning to new algorithms and systems for large and complex tasks.
We are beginning to harness the power of AI across multiple domains. Machine learning and data science are becoming a core part of many Microsoft systems and products. But these techniques also raise complex ethical and social questions. How can we best use AI and machine learning systems to assist people and offer enhanced insights while also avoiding exposing them to different types of discrimination in health, housing, law enforcement and employment?
Labs: New York
We are a research and development team within the Microsoft Health organization in TnR (Technology & Research). We are focused on delivering health-related insights, inferences, and experiences to Microsoft Health, Band, Cortana, and other products.
The key to intelligence is learning how to interact with complex, uncertain, and changing environments. We tackle these problems using approaches from machine learning and reinforcement learning, game theory, and cognitive science. We explore the foundations of machine intelligence, and develop the technology that will power tomorrow's AI.
The Systems research group at MSR NYC investigates a wide variety of topics within distributed systems, operating systems, and networking, as well as the theory relevant to these areas. In addition to "traditional" systems research, we also engage in deep collaborations with other research groups in the NYC lab, including world experts in machine learning, computational social science, and economics.
Labs: New York
The Nature + Computing team consists of individuals in a diverse range of Redmond-area MSR groups who apply the tools of data science to scientific data and whose research interests are focused on the study of nature. This group includes individuals whose long term research agenda is centered on studying nature, as well as individuals who for a time are looking to the natural sciences as application domains in which to test their technologies.
Microeconomics research group is a collection of economists in MSR and the Office of the Chief Economist.
The focus of the Machine Teaching Group is to make the process of training a machine easy, fast and universally accessible. This multi-disciplinary challenge lies at the intersection of Machine Learning, Human-Computer Interaction, Visualization and Engineering.
Understanding, analyzing and processing data is at the core of building most of the modern large scale systems and intelligent applications. We work on various aspects of data-driven algorithms including fast machine learning algorithms that can learn from large scale and noisy data, learning from natural language and multilingual data, and applications of these algorithms to Web Search, Computational Advertisement, Recommendation Systems, Natural Language Systems, Music Retrieval, etc.
Systems work at MSR India covers a broad spectrum of areas ranging from program verification, programming languages and tools, distributed systems, networking and security. We are interested in both building real world systems and studying the principles behind how to design them.
A focus of ours has been developing mathematical models under which simple algorithms (often ones used widely in practice) have provable guarantees of time and space. Researchers at MSR India started the use of sampling from the input to speed up matrix algorithms and this remains one of their interests.
Bringing innovations in the base abstractions from which developers build applications and practical implementations of those abstractions in operating systems. Our work spans from user interfaces to kernel and OS substructures. We are part of the MSR New Experiences and Technologies (NExT) organization.
We apply principles from computer science, machine learning, and statistics to genomics applications including sequence alignment, variant calling, denovo sequencing, and genome-wide association studies.
The speech group focuses on tools and algorithms that facilitate the development of innovative intelligent services using speech recognition, speech understanding and text-to-speech. The Arabic language has a special interest based on the lab’s wealth of tools for Arabic processing but we are also interested in quickly developing speech-based services in limited time and resource settings.
Labs: ATL Cairo
The NLP team at ATLC is focusing on building unsupervised pipelines for Text processing targeting a broad spectrum of tasks ranging from Input method editors to large-scale knowledge extraction.
Labs: ATL Cairo
The DLTC is managed by Li Deng, the Chief Scientist for AI. The goal of the DLTC is to build advanced deep learning technologies towards artificial general intelligence (AGI). We develop state-of-the-art algorithms and models in deep unsupervised learning, deep reinforcement learning, and deep learning applications in knowledge management and distillation, big data analytics, internet/enterprise information processing, natural language, vision, speech, and multimodal processing. We are hiring!
The AI group consists of an elite team of researchers who have strong expertise in artificial intelligence, machine learning, game theory, and information retrieval. The group is devoted to the following research directions: cloud computing, robot, game-theoretic machine learning, large-scale distributed computing, and deep learning techniques for text mining.
The Enable group focuses on creating technologies to help restore capabilities to people living with disabilities.
The Systems Research Group is devoted to significantly extending the state of the art in distributed systems and operating systems. Our aim is to make systems secure, scalable, fault-tolerant, manageable, and fast
The Audio and Acoustics group conducts research in audio processing and speech enhancement, 3D audio perception and technologies, devices for audio capture and rendering, array processing, information extraction from audio signals.
Our mission is to harvest and curate the wealth of knowledge encoded in language: people, content, things, connections, and activities. We mobilize research and advanced technology for the Technology arm of MSR by adapting, developing and integrating state-of-the-art technology from NLP, text mining, machine learning, knowledge extraction, and knowledge representation, while building end to end interactive knowledge experiences in close collaboration with partners across MSR and product teams.
The Knowledge Mining (KM) group at Microsoft Research Asia aims to understand and serve the world through knowledge discovery and data mining. It consists of a team of interdisciplinary researchers spanning data mining, machine learning, natural language processing, information retrieval and social computing areas.
Multimedia Search and Mining (MSM) group focuses on a wide variety of multimedia-related research and projects, e.g., understanding, analysis, search, data mining, and applications. We are working on research problems in image understanding, video analytics, large scale visual (image and video) indexing and search, 3D reconstruction, and so on.
Our goal is to extract biological and medical knowledge from text. Natural Language Processing tools and techniques are used in combination with biological resources.